LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios

• Published in: Computational economics Vol. 63; no. 4; pp. 1511 - 1542
• Main Authors: García-Medina, Andrés, Aguayo-Moreno, Ester
• Format: Journal Article
• Language: English
• Published: Netherlands Springer 01.04.2024; Springer Nature B.V; Springer US
• Subjects: Artificial neural networks; Autoregressive models; Capital; Crypto-currencies; Digital currencies; Forecasts and trends; Investment analysis; Machine learning; Multilayer perceptrons; Neural networks; Pandemics; Portfolios; Risk reduction; Short term memory; Time series; Variants; Volatility; Cryptocurrencies; GARCH–LSTM models; Volatility

In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity (GARCH) models, multilayer perceptron (MLP), long short-term memory (LSTM), and hybrid models of the type LSTM and GARCH, where parameters of the GARCH f...